Structured Citation Trend Prediction Using Graph Neural Networks
Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and practitioners. Prior citation prediction methods ofte...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2021-04 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | Cummings, Daniel Nassar, Marcel |
| description | Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and practitioners. Prior citation prediction methods often require initial citation trends to be established and do not take advantage of the recent advancements in graph neural networks (GNNs). We present GNN-based architecture that predicts the top set of papers at the time of publication. For experiments, we curate a set of academic citation graphs for a variety of conferences and show that the proposed model outperforms other classic machine learning models in terms of the F1-score. |
| doi_str_mv | 10.48550/arxiv.2104.02562 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_2104_02562</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2509443054</sourcerecordid><originalsourceid>FETCH-LOGICAL-a524-fc4be5f1acee437ced01d5b4f0727527edb045b4e8b644f61409baf9277ad5083</originalsourceid><addsrcrecordid>eNotj09LxDAUxIMguKz7ATxZ8Nz68vLStDel6K6wqGA9l7RNteva1iT1z7e3dj0NMwzD_Bg74xBRIiVcavvdfkbIgSJAGeMRW6AQPEwI8YStnNsBAMYKpRQLdvXk7Vj50Zo6yFqvfdt3QW5NVwePU9ZWc_Ds2u4lWFs9vAb3ZrR6P4n_6u2bO2XHjd47s_rXJctvb_JsE24f1nfZ9TbUEilsKiqNbLiujCGhKlMDr2VJDShUEpWpS6DJm6SMiZqYE6SlblJUStcSErFk54fZGa8YbPuu7U_xh1nMmFPj4tAYbP8xGueLXT_abvpUoISUSIAk8QtKx1U1</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2509443054</pqid></control><display><type>article</type><title>Structured Citation Trend Prediction Using Graph Neural Networks</title><source>arXiv.org</source><source>Free E- Journals</source><creator>Cummings, Daniel ; Nassar, Marcel</creator><creatorcontrib>Cummings, Daniel ; Nassar, Marcel</creatorcontrib><description>Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and practitioners. Prior citation prediction methods often require initial citation trends to be established and do not take advantage of the recent advancements in graph neural networks (GNNs). We present GNN-based architecture that predicts the top set of papers at the time of publication. For experiments, we curate a set of academic citation graphs for a variety of conferences and show that the proposed model outperforms other classic machine learning models in terms of the F1-score.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2104.02562</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Citation analysis ; Computer Science - Learning ; Computer Science - Social and Information Networks ; Graph neural networks ; Graphs ; Machine learning ; Neural networks ; Scientific papers ; Trends</subject><ispartof>arXiv.org, 2021-04</ispartof><rights>2021. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,784,885,27925</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.2104.02562$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.1109/ICASSP40776.2020.9054769$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Cummings, Daniel</creatorcontrib><creatorcontrib>Nassar, Marcel</creatorcontrib><title>Structured Citation Trend Prediction Using Graph Neural Networks</title><title>arXiv.org</title><description>Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and practitioners. Prior citation prediction methods often require initial citation trends to be established and do not take advantage of the recent advancements in graph neural networks (GNNs). We present GNN-based architecture that predicts the top set of papers at the time of publication. For experiments, we curate a set of academic citation graphs for a variety of conferences and show that the proposed model outperforms other classic machine learning models in terms of the F1-score.</description><subject>Citation analysis</subject><subject>Computer Science - Learning</subject><subject>Computer Science - Social and Information Networks</subject><subject>Graph neural networks</subject><subject>Graphs</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Scientific papers</subject><subject>Trends</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj09LxDAUxIMguKz7ATxZ8Nz68vLStDel6K6wqGA9l7RNteva1iT1z7e3dj0NMwzD_Bg74xBRIiVcavvdfkbIgSJAGeMRW6AQPEwI8YStnNsBAMYKpRQLdvXk7Vj50Zo6yFqvfdt3QW5NVwePU9ZWc_Ds2u4lWFs9vAb3ZrR6P4n_6u2bO2XHjd47s_rXJctvb_JsE24f1nfZ9TbUEilsKiqNbLiujCGhKlMDr2VJDShUEpWpS6DJm6SMiZqYE6SlblJUStcSErFk54fZGa8YbPuu7U_xh1nMmFPj4tAYbP8xGueLXT_abvpUoISUSIAk8QtKx1U1</recordid><startdate>20210406</startdate><enddate>20210406</enddate><creator>Cummings, Daniel</creator><creator>Nassar, Marcel</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20210406</creationdate><title>Structured Citation Trend Prediction Using Graph Neural Networks</title><author>Cummings, Daniel ; Nassar, Marcel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a524-fc4be5f1acee437ced01d5b4f0727527edb045b4e8b644f61409baf9277ad5083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Citation analysis</topic><topic>Computer Science - Learning</topic><topic>Computer Science - Social and Information Networks</topic><topic>Graph neural networks</topic><topic>Graphs</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Scientific papers</topic><topic>Trends</topic><toplevel>online_resources</toplevel><creatorcontrib>Cummings, Daniel</creatorcontrib><creatorcontrib>Nassar, Marcel</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Computer Science</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cummings, Daniel</au><au>Nassar, Marcel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Structured Citation Trend Prediction Using Graph Neural Networks</atitle><jtitle>arXiv.org</jtitle><date>2021-04-06</date><risdate>2021</risdate><eissn>2331-8422</eissn><abstract>Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and practitioners. Prior citation prediction methods often require initial citation trends to be established and do not take advantage of the recent advancements in graph neural networks (GNNs). We present GNN-based architecture that predicts the top set of papers at the time of publication. For experiments, we curate a set of academic citation graphs for a variety of conferences and show that the proposed model outperforms other classic machine learning models in terms of the F1-score.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2104.02562</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2021-04 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_arxiv_primary_2104_02562 |
| source | arXiv.org; Free E- Journals |
| subjects | Citation analysis Computer Science - Learning Computer Science - Social and Information Networks Graph neural networks Graphs Machine learning Neural networks Scientific papers Trends |
| title | Structured Citation Trend Prediction Using Graph Neural Networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T19%3A09%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Structured%20Citation%20Trend%20Prediction%20Using%20Graph%20Neural%20Networks&rft.jtitle=arXiv.org&rft.au=Cummings,%20Daniel&rft.date=2021-04-06&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2104.02562&rft_dat=%3Cproquest_arxiv%3E2509443054%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2509443054&rft_id=info:pmid/&rfr_iscdi=true |